Applications involving multiple views from multiple directions of the same scene, such as stereo imaging in the meaning of stereoscopic or 3-D imaging, or applications involving multiple views of a scene (static or non-static) at different time instants while scene elements are changing, such as feature film production, video capturing or broadcasting, or applications involving multiple versions of originally the same image, such as two different scans of the same film negative, suffer from geometric differences and colour differences between different images. Stereoscopic imaging or real 3D requires a minimum of two pictures simulating our two eyes, the left image and the right image. Geometric differences can be caused by parallax in case of stereo images and by cropping, zoom, rotation or other geometric transforms in case of film scans. In case of capturing views at different time instants, geometric differences can be caused by object or camera motion. Colour differences are caused for example by shading, change of illumination direction, colour or distribution, specular reflections, cast shadows and other photometric effects. Colour differences are also being caused for example by non-calibrated cameras, non-calibrated film scanners, automatic exposure settings, automatic white balancing or even physical light effects in the scene.
Colour difference compensation is often the first step in image or video signal processing of multiple views or stereoscopic pictures as other steps such as disparity estimation or data compression benefited from low colour difference. In case of feature film, video and broadcasting, colour differences can disturb artistically the produced content, notably when editing involves the temporal reordering of images.
One approach for the compensation of colour differences between images is colour mapping, also called color transfer or colour correction which is applied for colour transformation. Colour mapping has the task of remapping the colour channels of an image to be suitable for further colour signal processing, colour signal transmission or colour reproduction.
Colour mapping starts typically with finding Geometric Feature Correspondences, usually abbreviated GFC, using methods such as Scale Invariant Feature Transformation, usually abbreviated SIFT or simply using a normalized cross correlation.
GFC is a list of pairs of matched feature points in multiple image (or views). Each of these pair is usually called a sample. GFC allow coping with the geometric differences between at least two images. As GFC computation is not free from errors, some samples of the GFC are wrong and are so-called outliers. These outliers are either coming from spatial positioning error of feature points or an error (so called “false positives”) in the matching of feature points. Wrong samples of GFC, outliers, are not positioned on the same semantic image detail in the images such as in the left and the right images in the case of stereo imaging, or in any two or more images in case of scanning, motion picture, video and broadcasting.
In a next step, those outliers are usually removed. Such a outlier removal step is significant because for example, if a sample of GFC lies in a highly textured region, a small error in spatial position of this sample of GFC can generate a large error in corresponding colors so that improved outlier detection is desired.
Finally, the remaining samples of GFC, also called “cleaned” GFC, are used to build a set of tuples of corresponding colour in order to fit a colour mapping model or to build a colour look up table.
It is usual to remove outliers in GFC directly after their calculation. Such a method rejects a sample of GFC as outlier if it does not match a geometric transformation model that is estimated from all samples of GFC. But, the drawback is that the geometric transformation model may not be able to describe all geometric differences between images, notably at high depth dynamics.
It is also usual that to reject a sample of a “cleaned” GFC in the framework of robust estimation of an initial color mapping model. But, the drawback is to reject some samples of the “cleaned” GFC as outliers if an initial estimated colour mapping model is far from these samples of the “cleaned” GFC. Such method then misses a wide range of true outliers as well as mistakenly detects some truly valid samples of the “cleaned” GFC as outliers. Another usual error of this method is inconsistency between colour channels such as e.g. applying a colour mapping model per channel without cross checking of outlier decisions between colour channels.
It is an aspect of the invention to reduce the number of false negatives which means to reduce the number of cases where a sample of GFC is detected as valid but truly is an outlier. Reducing false negatives is important as the color mapping model can be influenced by those missed outliers.
It is a further aspect of the invention to reduce the number of false positives in outlier detection, which means to reduce the number of cases where a sample of GFC is detected as an outlier but truly is not an outlier to provide more valid information for an improved colour mapping model.